Extended reality based neuromotor rehabilitation

Abstract

A system can include one or more processors, coupled with memory, to receive, from extended reality equipment, a sensed movements of a hand of a patient from the extended reality equipment. The system can animate the hand of the patient on the extended reality equipment based on the sensed movements.

1 - 40 . (canceled) 
     
     
         41 . A system, comprising:
 one or more processors, coupled with memory, to:   receive, from extended reality equipment, sensed movements of a portion of a patient attempting to move a virtual object in a computer rendered environment displayed on the extended reality equipment;   generate, using the sensed movements, a three-dimensional frequency heat map indicating movements of the portion of the patient in the computer rendered environment; and   execute a model trained by machine learning using the three-dimensional frequency heat map to determine a level of rehabilitation of the patient.   
     
     
         42 . The system of  claim 41 , comprising:
 the one or more processors to:   execute the model to determine the level of rehabilitation of the patient based on a frequency heat map of a hand of a patient or a frequency heat map of a head of the patient individually; or   execute the model to determine the level of rehabilitation of the patient based on a combination of the frequency heat map of the hand of the patient or the frequency heat map of the head of the patient.   
     
     
         43 . The system of  claim 41 , comprising:
 the one or more processors to:   animate, on the extended reality equipment, a task to be completed by the patient;   compare the level of rehabilitation of the patient to a threshold; and   update, on the extended reality equipment, the task to increase a level of difficultly of the task responsive to a determination that the level of rehabilitation of the patient satisfies the threshold.   
     
     
         44 . The system of  claim 41 , comprising:
 the one or more processors to:   receive a training data set comprising a plurality of three-dimensional frequency heat maps tagged with a plurality of levels of rehabilitation of patients;   execute at least one machine learning technique to train the model using the training data set; and   deploy the model to determine the level of rehabilitation of the patient using the three- dimensional frequency heat map.   
     
     
         45 . The system of  claim 41 , comprising:
 the one or more processors to:   generate data to cause a graphical user interface to display on a user device;   the graphical user interface comprising a two-dimensional chart comprising two lateral axes to display an overhead view of the three-dimensional frequency heat map.   
     
     
         46 . The system of  claim 41 , comprising:
 the one or more processors to:   receive, from the extended reality equipment, first sensed movements of a right hand of the patient in the computer rendered environment and second sensed movements of a left hand of the patient in the computer rendered environment;   generate, using the first sensed movements, the three-dimensional frequency heat map indicating positions of the right hand in the computer rendered environment;   generate, using the second sensed movements, a second three-dimensional frequency heat map indicating positions of the left hand in the computer rendered environment;   execute the model trained by machine learning using the three-dimensional frequency heat map to determine the level of rehabilitation of the right hand of the patient; and   execute the model trained by machine learning using the second three-dimensional frequency heat map to determine a second level of rehabilitation of the left hand of the patient.   
     
     
         47 . The system of  claim 41 , comprising:
 the one or more processors to:   receive, from the extended reality equipment, first sensed movements of a right hand of the patient in the computer rendered environment and second sensed movements of a left hand of the patient in the computer rendered environment;   generate, using the first sensed movements, the three-dimensional frequency heat map indicating positions of the right hand in the computer rendered environment;   generate, using the second sensed movements, a second three-dimensional frequency heat map indicating positions of the left hand in the computer rendered environment;   compare the three-dimensional frequency heat map with the second three-dimensional frequency heat map to detect that the patient favors the right hand over the left hand; and   generate a set of tasks for the patient to complete in the computer rendered environment with the left hand responsive to a detection that the patient favors the right hand over the left hand.   
     
     
         48 . The system of  claim 41 , comprising:
 the one or more processors to:   receive, from the extended reality equipment, data indicating a position of a head of the patient;   track positions of the head of the patient using the data received from the extended reality equipment; and   determine the level of rehabilitation of the patient using the positions of the head of the patient.   
     
     
         49 . The system of  claim 41 , comprising;
 the one or more processors to:   receive, from the extended reality equipment, data indicating a distance of movement of a finger of the patient; and   determine, the level of rehabilitation of the patient using the distance of movement of the finger of the patient.   
     
     
         50 . The system of  claim 41 , comprising:
 the one or more processors to:   receive, from the extended reality equipment, data indicating movements of fingers of the patient; and   determine, using the data, a plurality of levels indicating an ability of the patient to make a plurality of hand poses.   
     
     
         51 . A method, comprising:
 receiving, by one or more processors, from extended reality equipment, sensed movements of a portion of a patient attempting to move a virtual object in a computer rendered environment displayed on the extended reality equipment;   generating, by the one or more processors, using the sensed movements, a three- dimensional frequency heat map indicating movements of the portion of the patient in the computer rendered environment; and   executing, by the one or more processors, a model trained by machine learning using the three-dimensional frequency heat map to determine a level of rehabilitation of the patient.   
     
     
         52 . The method of  claim 51 , comprising:
 animating, by the one or more processors, on the extended reality equipment, a task to be completed by the patient;   comparing, by the one or more processors, the level of rehabilitation of the patient to a threshold; and   updating, by the one or more processors, on the extended reality equipment, the task to increase a level of difficultly of the task responsive to a determination that the level of rehabilitation of the patient satisfies the threshold.   
     
     
         53 . The method of  claim 51 , comprising:
 receiving, by the one or more processors, a training data set comprising a plurality of three-dimensional frequency heat maps tagged with a plurality of levels of rehabilitation of patients;   executing, by the one or more processors, at least one machine learning technique to train the model using the training data set; and   deploying, by the one or more processors, the model to determine the level of rehabilitation of the patient using the three-dimensional frequency heat map.   
     
     
         54 . The method of  claim 51 , comprising:
 generating, by the one or more processors, data to cause a graphical user interface to display on a user device;   the graphical user interface comprising a two-dimensional chart comprising two lateral axes to display an overhead view of the three-dimensional frequency heat map.   
     
     
         55 . The method of  claim 51 , comprising:
 receiving, by the one or more processors, from the extended reality equipment, first sensed movements of a right hand of the patient in the computer rendered environment and second sensed movements of a left hand of the patient in the computer rendered environment;   generating, by the one or more processors, using the first sensed movements, the three- dimensional frequency heat map indicating positions of the right hand in the computer rendered environment;   generating, by the one or more processors, using the second sensed movements, a second three-dimensional frequency heat map indicating positions of the left hand in the computer rendered environment;   executing, by the one or more processors, the model trained by machine learning using the three-dimensional frequency heat map to determine the level of rehabilitation of the right hand of the patient; and   executing, by the one or more processors, the model trained by machine learning using the second three-dimensional frequency heat map to determine a second level of rehabilitation of the left hand of the patient.   
     
     
         56 . The method of  claim 51 , comprising:
 receiving, by the one or more processors, from the extended reality equipment, first sensed movements of a right hand of the patient in the computer rendered environment and second sensed movements of a left hand of the patient in the computer rendered environment;   generating, by the one or more processors, using the first sensed movements, the three-dimensional frequency heat map indicating positions of the right hand in the computer rendered environment;   generating, by the one or more processors, using the second sensed movements, a second three-dimensional frequency heat map indicating positions of the left hand in the computer rendered environment;   comparing, by the one or more processors, the three-dimensional frequency heat map with the second three-dimensional frequency heat map to detect that the patient favors the right hand over the left hand; and   generating, by the one or more processors, a set of tasks for the patient to complete in the computer rendered environment with the left hand responsive to a detection that the patient favors the right hand over the left hand.   
     
     
         57 . The method of  claim 51 , comprising:
 receiving, by the one or more processors, from the extended reality equipment, data indicating a position of a head of the patient;   tracking, by the one or more processors, positions of the head of the patient using the data received from the extended reality equipment; and   determining, by the one or more processors, the level of rehabilitation of the patient using the positions of the head of the patient.   
     
     
         58 . The method of  claim 51 , comprising;
 receiving, by the one or more processors, from the extended reality equipment, data indicating a distance of movement of a finger of the patient; and   determining, by the one or more processors, the level of rehabilitation of the patient using the distance of movement of the finger of the patient.   
     
     
         59 . The method of  claim 51 , comprising:
 receiving, by the one or more processors, from the extended reality equipment, data indicating movements of fingers of the patient; and   determining, by the one or more processors, using the data, a plurality of levels indicating an ability of the patient to make a plurality of hand poses.   
     
     
         60 . One or more non-transitory computer readable media storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to perform operations, comprising:
 receiving from extended reality equipment, sensed movements of a portion of a patient attempting to move a virtual object in a computer rendered environment displayed on the extended reality equipment;   generating using the sensed movements, a three-dimensional frequency heat map indicating movements of the portion of the patient in the computer rendered environment; and   executing a model trained by machine learning using the three-dimensional frequency heat map to determine a level of rehabilitation of the patient.   
     
     
         61 . (canceled)